Architectural Determinants of QA Model Robustness on CLIFT Versus General-Domain Benchmarks

Recent deep learning models for tabular data currently compete with the traditional ML models based on decision trees (GBDT). Unlike GBDT, deep models can additionally benefit from pretraining, which is a workhorse of DL for vision and NLP. For tabular problems, several pretraining methods were proposed, but it is not entirely clear if pretraining provides consistent noticeable improvements and what method should be used, since the methods are often not compared to each other or comparison is limited to the simplest MLP architectures. In this work, we aim to identify the best practices to pr

Research goal: How do different QA model architectures (e.g., BERT, RoBERTa, T5) perform on the CLIFT benchmark compared to their performance on general-domain QA benchmarks like SQuAD or HotpotQA, and what architectural features contribute most to robustness under distribution shift?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.7/10.